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#
# Copyright 2007-2019 by the individuals mentioned in the source code history
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# -----------------------------------------------------------------------
# Program: BivariateHeterogeneity_PathRaw.R
# Author: Hermine Maes
# Date: 2009.08.01
#
# ModelType: Heterogeneity
# DataType: Continuous
# Field: None
#
# Purpose:
# Bivariate Heterogeneity model to test differences in means and variances across multiple groups
# Path style model input - Raw data input
#
# RevisionHistory:
# Hermine Maes -- 2009.10.08 updated & reformatted
# Ross Gore -- 2011.06.15 added Model, Data & Field metadata
# Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------
require(OpenMx)
require(MASS)
# Load Libraries
# -----------------------------------------------------------------------------
if (0) {
set.seed(200)
rs=.5
xy1 <- mvtnorm::rmvnorm (1000, c(0,0), matrix(c(1,rs,rs,1),2,2))
set.seed(200)
# group 1
# -------------------------------------
rs=.4
xy2 <- mvtnorm::rmvnorm (1000, c(0,0), matrix(c(1,rs,rs,1),2,2))
# group 2
# -------------------------------------
xy1 <- round(xy1, 12)
write.csv(xy1, "data/BivariateHeterogeneity1.csv", row.names=FALSE)
xy2 <- round(xy2, 12)
write.csv(xy2, "data/BivariateHeterogeneity2.csv", row.names=FALSE)
stop("data generated")
} else {
xy1 <- as.matrix(read.csv("data/BivariateHeterogeneity1.csv"))
xy2 <- as.matrix(read.csv("data/BivariateHeterogeneity2.csv"))
}
selVars <- c("X","Y")
summary(xy1)
cov(xy1)
dimnames(xy1) <- list(NULL, selVars)
summary(xy2)
cov(xy2)
dimnames(xy2) <- list(NULL, selVars)
# Print Descriptive Statistics
# -------------------------------------
# Simulate Data
# -----------------------------------------------------------------------------
dataRaw1 <- mxData( observed=xy1, type="raw")
variances1 <- mxPath( from=selVars, arrows=2,
free = TRUE, values=1, lbound=.01, labels=c("vX1","vY1") )
covariance1 <- mxPath( from="X", to="Y", arrows=2,
free = TRUE, values=.2, lbound=.01, labels="cXY1")
means1 <- mxPath( from="one", to=selVars, arrows=1,
free = TRUE, values=c(0.1,-0.1), ubound=c(NA,0), lbound=c(0,NA),
labels=c("mX1","mY1") )
model1 <- mxModel("group1", type="RAM", manifestVars= selVars,
dataRaw1, variances1, covariance1, means1)
dataRaw2 <- mxData( observed=xy2, type="raw")
variances2 <- mxPath( from=selVars, arrows=2,
free = TRUE, values=1, lbound=.01, labels=c("vX2","vY2") )
covariance2 <- mxPath( from="X", to="Y", arrows=2,
free = TRUE, values=.2, lbound=.01, labels="cXY2")
means2 <- mxPath( from="one", to=selVars, arrows=1,
free = TRUE, values=c(0.1,-0.1), ubound=c(NA,0), lbound=c(0,NA),
labels=c("mX2","mY2") )
model2 <- mxModel("group2", type="RAM", manifestVars= selVars,
dataRaw2, variances2, covariance2, means2)
#h12 <- mxAlgebra( group1.fitfunction + group2.fitfunction, name="h12" )
#funML <- mxFitFunctionAlgebra("h12")
fun <- mxFitFunctionMultigroup(c("group1", "group2"))
bivHetModel <- mxModel("bivariate Heterogeneity Path Specification",
model1, model2, fun )
bivHetFit <- mxRun(bivHetModel)
hetExp <- mxGetExpected(bivHetFit, c('covariance', 'means'))
EM1Het <- hetExp$group1$means
EM2Het <- hetExp$group2$means
EC1Het <- hetExp$group1$covariance
EC2Het <- hetExp$group2$covariance
LLHet <- -2*logLik(bivHetFit)
EM1Het; EM2Het; EC1Het; EC2Het; LLHet
# Fit Heterogeneity Model
# -----------------------------------------------------------------------------
bivHomModel <- bivHetModel
bivHomModel[['group2.S']]$labels <- bivHomModel[['group1.S']]$labels
bivHomModel[['group2.M']]$labels <- bivHomModel[['group1.M']]$labels
bivHomFit <- mxRun(bivHomModel)
homExp <- mxGetExpected(bivHomFit, c('covariance', 'means'))
EM1Hom <- homExp$group1$means
EM2Hom <- homExp$group2$means
EC1Hom <- homExp$group1$covariance
EC2Hom <- homExp$group2$covariance
LLHom <- -2*logLik(bivHomFit)
EM1Hom; EM2Hom; EC1Hom; EC2Hom; LLHom
Chi <- LLHom-LLHet
LRT <- rbind(LLHet,LLHom,Chi)
LRT
# Fit Homnogeneity Model
# -----------------------------------------------------------------------------
omxCheckCloseEnough(LLHet, 10944.8728, .001)
omxCheckCloseEnough(c(EC1Het), c(1.0093, 0.4813, 0.4813, 0.9935), .001)
omxCheckCloseEnough(c(EM1Het), c(0.0321, -0.0049), .001)
omxCheckCloseEnough(c(EC2Het), c(1.0123, 0.3799, 0.3799, 0.9957),.001)
omxCheckCloseEnough(c(EM2Het), c(0.0334, -0.0071),.001)
omxCheckCloseEnough(LLHom, 10954.3676,.001)
omxCheckCloseEnough(c(EC1Hom), c(1.0108, 0.4306, 0.4306, 0.9946),.001)
omxCheckCloseEnough(c(EM1Hom), c(0.0328, -0.006),.001)
omxCheckCloseEnough(c(EC2Hom), c(1.0108, 0.4306, 0.4306, 0.9946),.001)
omxCheckCloseEnough(c(EM2Hom), c(0.0328, -0.006),.001)
# Compare OpenMx results to Mx results
# (LL: likelihood; expCov: expected covariance, expMean: expected means)
# -----------------------------------------------------------------------------
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